Self-adaptive generator set load adjustment and coal optimized supply control method

Abstract

The invention discloses a self-adaptive generator set load adjustment and coal optimized supply control method, and the method achieves the accurate prediction of short-term and medium-and-long-term load demands through the building of a self-adaptive generator set load adjustment and coal optimized supply control method, and a deep learning load prediction model of multi-dimensional data. And a hierarchical response mechanism and a real-time control algorithm are combined, so that the load response speed and the adjustment efficiency are improved. The dynamic coal purchase and supply plan is combined with the reinforcement learning algorithm, the coal inventory can be dynamically adjusted according to the load demand, and the supply efficiency and economy are optimized. Coal quality monitoring equipment is introduced, high-calorific-value coal is distributed to a high-load unit in a targeted mode, the combustion efficiency is improved, and coal consumption and equipment abrasion are reduced. A closed-loop feedback system and a self-learning mechanism realize real-time monitoring and automatic adjustment, and the prediction precision and adaptability are improved.

Description

Technical Field

[0001] The present application relates to the technical field of adaptive load adjustment of power generation units and optimized coal supply control, and in particular to an adaptive load adjustment of power generation units and optimized coal supply control method. Background Art

[0002] With the growth of electricity demand and the limitation of energy resources, the operation efficiency and resource management of power systems have become core issues in the field of power generation. Traditional power generation units mainly rely on manual or fixed control strategies for load adjustment and coal supply management, which usually cannot achieve a rapid response to load changes, resulting in reduced system efficiency, waste of resources and increased equipment wear.

[0003] In actual operation, power plants need to reasonably allocate the load of each generator set according to the real-time changes in the grid load to meet the power demand and optimize the use of coal resources. However, the load allocation and coal supply in traditional methods usually adopt fixed preset strategies or empirical rules, lacking dynamic adaptability to load changes and coal supply status, resulting in slow system response and insufficient resource utilization, especially when dealing with sudden load fluctuations, which easily leads to stability problems.

[0004] In addition, traditional coal supply management is usually based on fixed inventory or regular replenishment, which cannot be dynamically adjusted according to load forecast results and coal quality, making it difficult to balance power generation efficiency and economy. Coal quality has a direct impact on combustion efficiency during the power generation process. If coal is not classified or distributed unreasonably, it is easy to increase coal consumption and reduce power generation efficiency, further increasing resource waste and cost burden.

[0005] At the same time, there is often a lack of a closed-loop feedback mechanism during the operation of power plants, making it difficult to adjust load or coal supply strategies based on real-time operating data. This control mode that lacks data feedback leads to insufficient optimization of the system in terms of efficiency, energy consumption and cost. Summary of the invention

[0006] The present application provides a method, device, computer equipment and storage medium for adaptive load adjustment of power generation units and optimized coal supply control, aiming to solve the problem that load distribution and coal supply in traditional methods usually adopt fixed preset strategies or empirical rules, lack dynamic adaptability to load changes and coal supply status, resulting in slow system response speed and insufficient resource utilization, especially prone to stability problems when dealing with sudden load fluctuations.

[0007] An adaptive generator set load adjustment and coal optimization supply control method, the method comprising:

[0008] Establish a load forecasting model based on multi-dimensional data, analyze power demand, meteorological conditions and historical load data through deep learning algorithms, and generate short-term and medium-term load forecast results;

[0009] Based on the load forecasting result, load distribution is performed on the generator sets according to a hierarchical response mechanism, and the generator sets are divided into basic load units and regulating load units;

[0010] Adjust the load distribution of the generator set through real-time load control algorithm and determine the load distribution plan using multi-objective optimization algorithm;

[0011] Develop dynamic coal procurement and supply plans based on load demand data and coal supply conditions, and optimize supplier management and inventory;

[0012] Establish a closed-loop feedback system to collect feedback data such as load data, coal consumption and power generation efficiency, and adjust load distribution, coal input and supply chain management based on the feedback data.

[0013] In the above scheme, optionally, the load forecasting model adopts a long short-term memory (LSTM) network or a gated recurrent unit (GRU) network to improve the forecasting accuracy through self-learning optimization of historical data and adapt to changes in load demand.

[0014] In the above scheme, optionally, the hierarchical response mechanism includes dividing the generator sets into high-efficiency basic load units and fast-adjusting load units, the basic load units are used to stabilize load demand, and the adjusting load units are used to respond to load fluctuations.

[0015] In the above scheme, optionally, the real-time load control algorithm determines the optimal load distribution scheme to reduce coal consumption and equipment wear through a multi-objective optimization algorithm, including a genetic algorithm or a particle swarm algorithm.

[0016] In the above scheme, optionally, the coal supply control module includes dynamic supply chain management and intelligent procurement algorithms, and uses reinforcement learning algorithms to generate coal procurement and transportation plans based on load forecasts and inventory data.

[0017] In the above scheme, optionally, the coal quality monitoring equipment includes an X-ray fluorescence spectrometer for detecting parameters such as calorific value, ash content, moisture content and volatile matter of coal.

[0018] In the above scheme, optionally, the coal is stored and distributed in a graded manner based on the detection results of the coal quality monitoring equipment, and high calorific value coal is distributed to high-load units, and low calorific value coal is distributed to low-load or standby units.

[0019] In the above scheme, optionally, the closed-loop feedback system includes a multi-level feedback control loop for collecting data such as unit load, coal consumption and power generation efficiency, and adjusting load distribution and supply scheduling based on error feedback.

[0020] In the above scheme, optionally, the closed-loop feedback system includes a data self-learning mechanism to optimize the accuracy and adaptability of the prediction model through historical feedback data and load prediction errors.

[0021] In the above scheme, optionally, the full life cycle optimization includes a big data analysis module, which performs big data analysis on data such as load distribution, coal consumption, unit maintenance cycle and carbon emissions, and generates optimization strategies through cloud computing.

[0022] Compared with the prior art, this application has at least the following beneficial effects:

[0023] Based on further analysis and research on the problems of the prior art, this application recognizes that the prior art lacks dynamic adaptability to load changes and coal supply status, resulting in slow system response speed and insufficient resource utilization, especially when dealing with sudden load fluctuations, which is prone to stability problems. By establishing an adaptive load adjustment method for generator sets and coal optimization supply control method, the problems of slow load response, insufficient utilization of coal resources and lack of dynamic feedback mechanism in traditional power generation systems are effectively solved. First, the deep learning load forecasting model of multi-dimensional data is used to accurately predict short-term and medium- and long-term load demand, and the load response speed and adjustment efficiency are improved by combining the hierarchical response mechanism and real-time control algorithm. Secondly, the dynamic coal procurement and supply plan combined with the reinforcement learning algorithm can dynamically adjust the coal inventory according to the load demand and optimize the supply efficiency and economy. In addition, the introduction of coal quality monitoring equipment can allocate high calorific value coal to high-load units in a targeted manner, improve combustion efficiency, and reduce coal consumption and equipment wear. Finally, the closed-loop feedback system and self-learning mechanism realize real-time monitoring and automatic adjustment, which improves the prediction accuracy and adaptability. Overall, this solution not only improves the responsiveness and stability of the power generation system, but also maximizes resource utilization and reduces costs, significantly improving the shortcomings of traditional power generation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flow chart of a method for adaptive generator set load adjustment and coal optimization supply control provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0026] In one embodiment, Figure 1 As shown, a method for adaptively adjusting load of a generator set and optimizing coal supply control is provided, comprising the following steps:

[0027] Establish a load forecasting model based on multi-dimensional data, analyze power demand, meteorological conditions and historical load data through deep learning algorithms, and generate short-term and medium-term load forecast results;

[0028] Based on the load forecasting result, load distribution is performed on the generator sets according to a hierarchical response mechanism, and the generator sets are divided into basic load units and regulating load units;

[0029] Adjust the load distribution of the generator set through real-time load control algorithm and determine the load distribution plan using multi-objective optimization algorithm;

[0030] Develop dynamic coal procurement and supply plans based on load demand data and coal supply conditions, and optimize supplier management and inventory;

[0031] Establish a closed-loop feedback system to collect feedback data such as load data, coal consumption and power generation efficiency, and adjust load distribution, coal input and supply chain management based on the feedback data.

[0032] The technical solution described in this embodiment can effectively solve the problems of slow load response, insufficient utilization of coal resources and lack of dynamic feedback mechanism in the background technology by establishing an adaptive generator load adjustment and coal optimization supply control method. The specific technical effects are as follows:

[0033] Improve load response speed and adjustment efficiency: Through the deep learning load forecasting model based on multi-dimensional data, accurate prediction of short-term and medium-term load demand can be achieved. Combined with the hierarchical response mechanism and real-time load control algorithm, the load distribution of the generator set is more flexible, which can quickly respond to the dynamic changes of the grid load and reduce the impact on system stability.

[0034] Optimize coal supply management: This solution generates procurement and supply chain management solutions through dynamic coal procurement and supply planning combined with reinforcement learning algorithms. It can dynamically adjust coal inventory based on load demand forecast data to avoid excessive reserves or shortages of coal resources, thereby optimizing coal supply efficiency and economy.

[0035] Improve coal combustion efficiency: By introducing coal quality monitoring equipment to detect coal calorific value, ash content and other parameters in real time, and by classifying and storing coal and intelligently allocating it to different load units, high calorific value coal can be allocated to high load units in a targeted manner, further improving combustion efficiency and reducing coal consumption and equipment wear.

[0036] Achieve closed-loop feedback control and adaptive optimization: Through the closed-loop feedback system's multi-level feedback control loop and data self-learning mechanism, it can automatically adjust load distribution and coal supply management based on real-time monitoring of load, coal consumption, power generation efficiency and other data. The system can also adaptively optimize the prediction model through historical data to improve prediction accuracy and adaptability, thereby continuously optimizing energy consumption and costs in the power generation process throughout the life cycle.

[0037] This embodiment not only improves the responsiveness and stability of the power generation system by adaptively adjusting the load and optimizing the coal supply, but also maximizes resource utilization and reduces costs, effectively solving the problems of delayed load response, inefficient coal management, and insufficient feedback optimization in traditional power generation systems.

[0038] In one embodiment, the load forecasting model uses a long short-term memory network or a gated recurrent unit network to improve forecasting accuracy through self-learning optimization of historical data and adapt to changes in load demand.

[0039] The load forecasting model uses a long short-term memory (LSTM) network or a gated recurrent unit (GRU) network to achieve higher load forecasting accuracy through self-learning optimization of historical data. The significant technical effect of this technical solution is that it improves the accuracy, adaptability and efficiency of load forecasting, providing a reliable basis for load adjustment and resource scheduling of power generation systems.

[0040] LSTM and GRU networks have significant advantages in processing time series data, especially for data with time dependence and periodic fluctuations such as load demand. LSTM networks can effectively handle long-term dependencies through their special memory gating structure, while GRU networks are optimized in performance and efficiency through simplified gating mechanisms, making them suitable for processing short-term dynamic changes. Through this type of deep learning algorithm, the load forecasting model can not only capture the long-term trend of the load, but also accurately predict rapid fluctuations in the short term, which helps the power generation system to respond and adjust quickly.

[0041] In addition, the self-learning optimization mechanism further enhances the adaptability of the model. The self-learning mechanism enables the model to automatically update parameters from the continuously accumulated real-time data and historical data to adapt to changes in load demand. Through self-learning optimization, the prediction model can dynamically adjust weight parameters to capture emerging load patterns, improve the model's prediction accuracy and adaptability to complex load fluctuations. This self-learning mechanism enables the model to respond to changes in power demand at any time without frequent manual intervention, with a high level of automation.

[0042] By improving the load forecasting accuracy, this embodiment greatly improves the decision-making quality of the system in load adjustment and coal supply management. Accurate load forecasting provides a reliable data basis for real-time load control, dynamic coal procurement and supply chain optimization, reducing load fluctuations and resource waste caused by inaccurate forecasts. In addition, this high-precision load forecast can also reduce the frequent start and stop of generator sets, effectively extend the service life of equipment, and reduce maintenance costs, thereby reducing the operating costs and resource consumption of the system throughout the life cycle. The load forecasting model can significantly improve the forecasting accuracy, adaptability and automation level, provide a stable and efficient load adjustment and resource optimization management basis for the power generation system, and effectively support the efficient implementation of subsequent load control and supply chain management.

[0043] In one embodiment, the hierarchical response mechanism includes dividing the generator sets into high-efficiency basic load units and fast-adjusting load units, wherein the basic load units are used to stabilize load demand and the adjusting load units are used to respond to load fluctuations.

[0044] The hierarchical response mechanism described in this embodiment improves the load response speed and stability of the power generation system by dividing the generator sets into high-efficiency basic load units and fast-adjusting load units, and provides flexibility and pertinence in load fluctuation management. The application of this hierarchical response mechanism can effectively balance the efficiency and flexibility requirements of the power system, enabling the power supply to have a fast response capability under fluctuating load conditions while maintaining the efficient operation of the generator sets.

[0045] The high-efficiency basic load unit mainly bears the stable load demand and is suitable for scenarios where a certain power output is maintained for a long time. Since the basic load unit works under a relatively stable load, the start-stop frequency of its equipment is low, which can effectively reduce wear and increase the life of the equipment. In addition, the basic load unit usually uses a high-efficiency generator set to improve the utilization rate of unit energy consumption through long-term stable operation. The stable output of this unit provides a solid basic power guarantee for the power system, avoids frequent adjustments caused by load fluctuations, and helps save energy and maintenance costs. The regulating load unit is used to quickly respond to short-term load fluctuations. This unit has high start-stop flexibility and adjustment speed, and can quickly adjust the output power when the load demand changes to adapt to the dynamic changes of the power grid. By configuring the unit with high response speed as the regulating load unit, the system can achieve rapid power compensation when the load fluctuates and maintain the stability of the power grid. The hierarchical response mechanism enables the regulating load unit to bear the adjustment task of the fluctuating load, and the basic load unit continues to operate in a stable state, avoiding unnecessary start-stop and load adjustment. The hierarchical response mechanism not only improves the load response speed, but also improves the operation economy of the power system. By letting the load fluctuation be managed by the load adjustment unit, the basic load unit can continue to operate at a high efficiency, reducing fuel waste and equipment wear caused by fluctuating loads, thereby achieving efficient fuel utilization. At the same time, this hierarchical response load distribution method also reduces the operating burden of each unit and improves the operating life and maintenance efficiency of the system.

[0046] The hierarchical response mechanism in this embodiment effectively solves the impact of load fluctuations on the stability and efficiency of generator sets through a flexible combination of base load and regulating load, enhances the system's responsiveness and operational economy, and provides important technical guarantees for the stable and efficient operation of the power system.

[0047] In one embodiment, the real-time load control algorithm determines the optimal load distribution scheme to reduce coal consumption and equipment wear through a multi-objective optimization algorithm, including a genetic algorithm or a particle swarm algorithm.

[0048] The application of the multi-objective optimization algorithm in load control of this embodiment can comprehensively consider multiple key parameters (such as load demand, coal consumption and equipment wear), so as to achieve global optimization of parameters in the process of load adjustment. By simulating the natural selection process, the genetic algorithm can find the optimal load distribution solution in a larger solution space, effectively balance the load of each generator set, and reduce the operating cost. At the same time, the particle swarm algorithm has the characteristics of fast convergence, which is suitable for real-time load adjustment and can quickly find the optimal solution that meets the system operation requirements. This optimization control not only improves the accuracy of load distribution, but also ensures the real-time responsiveness of the system.

[0049] In terms of reducing coal consumption, the real-time load control algorithm uses precise load distribution to prioritize efficient units to take on more load tasks, thus avoiding excessive operation of inefficient units. This distribution method ensures efficient use of energy and reduces unnecessary fuel consumption. In addition, the algorithm reasonably distributes load among different units, avoiding frequent start-up and shutdown of each unit and drastic load fluctuations, thereby achieving lower coal consumption and improving combustion efficiency.

[0050] In terms of equipment wear control, the real-time load control algorithm can dynamically adjust the operating status of each unit according to the equipment status and load demand, reduce the long-term operation of the equipment under high load conditions, and reduce mechanical wear. At the same time, the multi-objective optimization algorithm is used to achieve balanced load distribution among different units, avoiding excessive load or frequent start and stop of specific units, thereby effectively reducing the wear rate of equipment and improving the stability of the system and the service life of the equipment.

[0051] The real-time load control algorithm achieves efficient load distribution through multi-objective optimization, reduces coal consumption, reduces equipment wear, and extends the service life of the unit, thereby improving the economy and durability of the system while ensuring the response speed of the power generation system. This load control method based on the optimization algorithm effectively improves the resource utilization efficiency of the power generation process and the reliability of equipment operation, laying a technical foundation for the efficient and stable operation of the power system.

[0052] In one embodiment, the coal supply control module includes dynamic supply chain management and intelligent procurement algorithms, which use reinforcement learning algorithms to generate coal procurement and transportation plans based on load forecasts and inventory data.

[0053] The coal supply control module adopts dynamic supply chain management and intelligent procurement algorithms, and uses reinforcement learning algorithms to generate coal procurement and transportation plans based on load forecasts and inventory data. This technical solution significantly improves the adaptability and economy of coal supply, and has produced important technical effects in reducing supply chain risks and optimizing inventory management.

[0054] Traditional coal supply chain management is usually based on fixed procurement time and batch size, and does not have the ability to flexibly respond to demand fluctuations, which can easily lead to excessive or insufficient inventory. Dynamic supply chain management makes coal procurement more flexible by combining real-time load forecasting and inventory data. The system can increase procurement volume during peak load demand periods and reduce procurement volume when the load is low, maintaining a reasonable inventory level, thereby avoiding excessive storage or shortage of coal. This dynamic adjustment mechanism improves the accuracy of coal supply and provides a more stable fuel guarantee for the system.

[0055] The application of reinforcement learning algorithms further improves the intelligence level of procurement decisions. Through reinforcement learning, the system can gradually learn from historical data and optimize procurement decisions, so that each purchase can better adapt to the current load demand and inventory status. The reinforcement learning algorithm can dynamically adjust the procurement plan according to real-time data to achieve the global optimization of the supply chain. Reinforcement learning also has the characteristics of strong adaptability, and can continuously adjust strategies in the supply chain changes to adapt to different market conditions and price fluctuations. Intelligent procurement algorithms can effectively reduce procurement costs and avoid waste of resources through real-time analysis of data.

[0056] In addition, the dynamic supply chain management solution also has the effect of reducing transportation costs. When generating a coal transportation plan, the system can optimize logistics resources based on the economy and timeliness of the transportation route, avoid repeated transportation or lengthy routes, and improve transportation efficiency. At the same time, the intelligent scheduling system in supply chain management can coordinate transportation and procurement plans to achieve on-demand distribution of coal, further reducing energy consumption during transportation.

[0057] The dynamic supply chain management and intelligent procurement algorithm adopted in this embodiment effectively improves the responsiveness, economy and environmental friendliness of the coal supply chain. Through the combination of load forecasting and reinforcement learning algorithms, the solution can achieve efficient, accurate and energy-saving coal supply, providing an intelligent and dynamic supply chain management solution for the power generation system.

[0058] In one embodiment, the coal quality monitoring equipment includes an X-ray fluorescence spectrometer for detecting parameters such as calorific value, ash content, moisture content and volatile matter of the coal.

[0059] The coal quality monitoring equipment in this embodiment realizes effective classification and reasonable allocation of coal through accurate real-time quality analysis, and enhances the combustion efficiency and resource utilization rate of the power generation process. This solution not only reduces coal consumption, but also improves the safety and durability of the equipment, providing key technical guarantees for the stable operation and environmental friendliness of the power generation system.

[0060] In one embodiment, coal is stored and distributed in a graded manner based on the detection results of the coal quality monitoring equipment, with high calorific value coal distributed to high-load units and low calorific value coal distributed to low-load or standby units.

[0061] The hierarchical storage and distribution technical solution in this embodiment realizes the efficient utilization and refined management of coal resources, and provides economic, environmentally friendly and reliable operation guarantees for the power generation system by improving combustion efficiency, reducing fuel consumption and reducing equipment wear.

[0062] In one embodiment, the closed-loop feedback system includes a multi-level feedback control loop for collecting data such as unit load, coal consumption and power generation efficiency, and adjusting load distribution and supply scheduling based on error feedback.

[0063] The closed-loop feedback system of this embodiment achieves precise control and real-time response of the power generation system through a multi-level feedback control loop and an error feedback mechanism. This solution effectively improves the adaptability and accuracy of load distribution and supply scheduling, and provides technical support for the efficient, economical and stable operation of the power generation system.

[0064] In one embodiment, the closed-loop feedback system includes a data self-learning mechanism to optimize the accuracy and adaptability of the prediction model through historical feedback data and load prediction errors.

[0065] In one embodiment, the full life cycle optimization includes a big data analysis module, which performs big data analysis on data such as load distribution, coal consumption, unit maintenance cycle and carbon emissions, and generates optimization strategies through cloud computing.

[0066] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. An adaptive generator load adjustment and coal supply optimization control method, characterized in that: The method comprises the following steps: Establish a load forecasting model based on multi-dimensional data, analyze power demand, meteorological conditions and historical load data through deep learning algorithms, and generate short-term and medium-term load forecast results; Based on the load forecasting result, load distribution is performed on the generator sets according to a hierarchical response mechanism, and the generator sets are divided into basic load units and regulating load units; Adjust the load distribution of the generator set through real-time load control algorithm and determine the load distribution plan using multi-objective optimization algorithm; Develop dynamic coal procurement and supply plans based on load demand data and coal supply conditions, and optimize supplier management and inventory; Establish a closed-loop feedback system to collect feedback data such as load data, coal consumption and power generation efficiency, and adjust load distribution, coal input and supply chain management based on the feedback data.

2. The method according to claim 1, characterized in that The load forecasting model adopts a long short-term memory network or a gated recurrent unit network, improves the forecasting accuracy through self-learning optimization of historical data, and adapts to changes in load demand.

3. The method according to claim 1, characterized in that The hierarchical response mechanism includes dividing the generator sets into high-efficiency basic load units and fast-adjusting load units, wherein the basic load units are used to stabilize load demand and the adjusting load units are used to respond to load fluctuations.

4. The method according to claim 1, characterized in that: The real-time load control algorithm determines the optimal load distribution scheme to reduce coal consumption and equipment wear through a multi-objective optimization algorithm, including a genetic algorithm or a particle swarm algorithm.

5. The method according to claim 1, characterized in that The coal supply control module includes dynamic supply chain management and intelligent procurement algorithms, and uses reinforcement learning algorithms to generate coal procurement and transportation plans based on load forecasts and inventory data.

6. The method according to claim 1, characterized in that The method also includes an X-ray fluorescence spectrometer for detecting the calorific value, ash content, moisture content and volatile matter parameters of the coal.

7. The method according to claim 6, characterized in that Coal is stored and distributed in stages based on the test results of coal quality monitoring equipment, with high calorific value coal allocated to high-load units and low calorific value coal allocated to low-load or standby units.

8. The method according to claim 1, characterized in that The closed-loop feedback system includes a multi-level feedback control loop for collecting data such as unit load, coal consumption and power generation efficiency, and adjusting load distribution and supply scheduling based on error feedback.

9. The method according to claim 1, characterized in that: The closed-loop feedback system includes a data self-learning mechanism to optimize the accuracy and adaptability of the prediction model through historical feedback data and load prediction errors.

10. The method according to claim 1, characterized in that The method also includes using a big data analysis module to perform big data analysis on data such as load distribution, coal consumption, unit maintenance cycle and carbon emissions, and generate optimization strategies through cloud computing.

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